InfraGuard.AI – IoT-Based Predictive Maintenance System for Public Infrastructure Safety and Efficiency

Public infrastructures, like bridges and roads, should be regularly maintained to ensure safety and function. Traditionally, fixed-interval schedules or reactive maintenance systems are followed, making them highly inefficient and leading to expenditure. In this project, a predictive maintenance system will be developed to ascertain the need for maintenance using real-time data from IoT sensors and historical maintenance records. This means that predictive maintenance using IoT can help governments schedule maintenance time efficiently, save resources, and maximize safety and life expectancy for public assets.

 Week 1-2: Initial Planning and Requirement Analysis

Define project objectives, scope, and high-level requirements.

Gather all necessary data and resources  Week 3-4: Design Phase

System architecture and data flow design

Dashboard interface design by wireframing and mock-ups  Week 5-6: Development Phase - IoT Sensors and Data Processing

IoT sensor installation for infrastructure condition monitoring

Develop the data processing system on how sensor data was collected and stored  Week 7-8: Development Phase — Machine Learning Models

Implement machine learning models that forecast maintenance needs

Train and validate models against historical data for maintenance  Week 9-10: Dashboard Development and Integration

Build a monitoring dashboard that can enable real-time decision-making

Integrate into existing maintenance management systems  Week 11-12: Testing and Refinement Phase

Integration testing to ensure functionality and usability

System refinement by performance metrics and user feedback

Compilation of final project report and documentation; presentation of individual reports by students.